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<link title="SigmoidMlPerceptron" rel="Chapter" href="SigmoidMlPerceptron.html"><title>Index of values</title>
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<center><h1>Index of values</h1></center>
<table>
<tr><td align="left"><br></td></tr>
<tr><td><a href="NeuralNetwork.html#VAL(+|)">(+|)</a> [<a href="NeuralNetwork.html">NeuralNetwork</a>]</td>
<td><div class="info">
Vectors addition
</div>
</td></tr>
<tr><td><a href="NeuralNetwork.html#VAL(-|)">(-|)</a> [<a href="NeuralNetwork.html">NeuralNetwork</a>]</td>
<td><div class="info">
Vectors substraction
</div>
</td></tr>
<tr><td><a href="NeuralNetwork.html#VAL(|>)">(|&gt;)</a> [<a href="NeuralNetwork.html">NeuralNetwork</a>]</td>
<td><div class="info">
Pipe operator
</div>
</td></tr>
<tr><td align="left"><br>A</td></tr>
<tr><td><a href="NeuralNetwork.html#VALarray_map2">array_map2</a> [<a href="NeuralNetwork.html">NeuralNetwork</a>]</td>
<td></td></tr>
<tr><td align="left"><br>B</td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALbias_of_string">bias_of_string</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td></td></tr>
<tr><td align="left"><br>C</td></tr>
<tr><td><a href="SigmoidMlPerceptron.html#VALcreate">create</a> [<a href="SigmoidMlPerceptron.html">SigmoidMlPerceptron</a>]</td>
<td><div class="info">
<code class="code">create input_size output_size layers_sizes weight bias</code>
	creates a sigmoid perceptron where layers_sizes : array of the sizes of the successive layers, all weights are equal to weight, all biases to bias
</div>
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<tr><td><a href="MlPerceptron.html#VALcreate">create</a> [<a href="MlPerceptron.html">MlPerceptron</a>]</td>
<td><div class="info">
<code class="code">create input_size output_size layers_sizes weight bias activation</code>
	creates a sigmoid multi-layer perceptron where layers_sizes : array of the sizes of the successive layers, all weights are equal to weight, all biases to bias
</div>
</td></tr>
<tr><td><a href="Adaline.html#VALcreate">create</a> [<a href="Adaline.html">Adaline</a>]</td>
<td><div class="info">
<code class="code">create input_size output_size weight bias</code>
creates an adaline object where
all weights are initialized to weight, all biases to bias
</div>
</td></tr>
<tr><td align="left"><br>F</td></tr>
<tr><td><a href="SigmoidMlPerceptron.html#VALfeed_and_sum_layer">feed_and_sum_layer</a> [<a href="SigmoidMlPerceptron.html">SigmoidMlPerceptron</a>]</td>
<td><div class="info">
<code class="code">feed_and_sum_layer input layer</code>
</div>
</td></tr>
<tr><td><a href="MlPerceptron.html#VALfeed_and_sum_layer">feed_and_sum_layer</a> [<a href="MlPerceptron.html">MlPerceptron</a>]</td>
<td><div class="info">
<code class="code">feed_and_sum_layer threshold_function input layer</code>
</div>
</td></tr>
<tr><td><a href="SigmoidMlPerceptron.html#VALfeed_layer">feed_layer</a> [<a href="SigmoidMlPerceptron.html">SigmoidMlPerceptron</a>]</td>
<td><div class="info">
<code class="code">feed_layer input layer</code>
</div>
</td></tr>
<tr><td><a href="MlPerceptron.html#VALfeed_layer">feed_layer</a> [<a href="MlPerceptron.html">MlPerceptron</a>]</td>
<td><div class="info">
<code class="code">feed_layer threshold_function input layer</code>
	returns the output of the layer
</div>
</td></tr>
<tr><td align="left"><br>I</td></tr>
<tr><td><a href="Adaline.html#VALinit">init</a> [<a href="Adaline.html">Adaline</a>]</td>
<td><div class="info">
<code class="code">create input_size output_size init_weight init_bias</code> creates an adaline object where :  <code class="code">weight = init_weight i j</code> where i is the layer index, j the neuron position , <code class="code">bias = init_bias i</code> where i is the layer index
</div>
</td></tr>
<tr><td><a href="NeuralNetwork.html#VALiter_random">iter_random</a> [<a href="NeuralNetwork.html">NeuralNetwork</a>]</td>
<td></td></tr>
<tr><td align="left"><br>L</td></tr>
<tr><td><a href="NeuralNetwork.html#VALlearn_base">learn_base</a> [<a href="NeuralNetwork.html">NeuralNetwork</a>]</td>
<td><div class="info">
<code class="code">learn_base perceptron rate base</code> makes the perceptron learn at the rate given on the learning base where base is an array of tuples : (input, desired output)
</div>
</td></tr>
<tr><td><a href="NeuralNetwork.html#VALlearn_random_base">learn_random_base</a> [<a href="NeuralNetwork.html">NeuralNetwork</a>]</td>
<td><div class="info">
<code class="code">learn_base perceptron rate base</code> makes the perceptron learn at the rate given on the learning base in a random order where base is an array of tuples : (input, desired output)
</div>
</td></tr>
<tr><td align="left"><br>O</td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALopenAdaline">openAdaline</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td><div class="info">
Reads an adaline network
</div>
</td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALopenFile">openFile</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td><div class="info">
Reads a perceptron file of unknown network type
</div>
</td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALopenMlPerceptron">openMlPerceptron</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td><div class="info">
Reads a multi-layer perceptron
</div>
</td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALopenSigmoidMlPerceptron">openSigmoidMlPerceptron</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td><div class="info">
Reads a sigmoid multi-layer perceptron
</div>
</td></tr>
<tr><td align="left"><br>R</td></tr>
<tr><td><a href="SigmoidMlPerceptron.html#VALrandom">random</a> [<a href="SigmoidMlPerceptron.html">SigmoidMlPerceptron</a>]</td>
<td><div class="info">
<code class="code">random input_size output_size layers_sizes weight_range bias_range</code>
	creates a sigmoid perceptron where layers_sizes : array of the sizes of the successive layers, weights_range : range of the random weights values around zero, bias_range : range of the biases values around zero
</div>
</td></tr>
<tr><td><a href="MlPerceptron.html#VALrandom">random</a> [<a href="MlPerceptron.html">MlPerceptron</a>]</td>
<td><div class="info">
<code class="code">random input_size output_size layers_sizes weight_range bias_range</code>
	creates a sigmoid perceptron where layers_sizes : array of the sizes of the successive layers, weights_range : range of the random weights values around zero, bias_range ; range of the biases values around zero
</div>
</td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALread_activation">read_activation</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td></td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALread_network_type">read_network_type</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td></td></tr>
<tr><td align="left"><br>S</td></tr>
<tr><td><a href="SigmoidMlPerceptron.html#VALsigderiv">sigderiv</a> [<a href="SigmoidMlPerceptron.html">SigmoidMlPerceptron</a>]</td>
<td><div class="info">
<code class="code">sigderiv(y) = sigmoid'(x)</code> where y = sigmoid(x)
</div>
</td></tr>
<tr><td><a href="SigmoidMlPerceptron.html#VALsigmoid">sigmoid</a> [<a href="SigmoidMlPerceptron.html">SigmoidMlPerceptron</a>]</td>
<td><div class="info">
Sigmoid function : 1 / ( 1 + exp(-x)).
</div>
</td></tr>
<tr><td><a href="SigmoidMlPerceptron.html#VALsigmoid'">sigmoid'</a> [<a href="SigmoidMlPerceptron.html">SigmoidMlPerceptron</a>]</td>
<td><div class="info">
Derivative of the sigmoid function
</div>
</td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALsplit_arrays">split_arrays</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td></td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALsplit_elem">split_elem</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td></td></tr>
<tr><td><a href="NeuralNetwork.html#VALstring_of_array">string_of_array</a> [<a href="NeuralNetwork.html">NeuralNetwork</a>]</td>
<td><div class="info">
</div>
</td></tr>
<tr><td><a href="NeuralNetwork.html#VALstring_of_weights">string_of_weights</a> [<a href="NeuralNetwork.html">NeuralNetwork</a>]</td>
<td><div class="info">
</div>
</td></tr>
<tr><td align="left"><br>W</td></tr>
<tr><td><a href="NeuralNetworkIo.html#VALweights_of_string">weights_of_string</a> [<a href="NeuralNetworkIo.html">NeuralNetworkIo</a>]</td>
<td></td></tr>
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